{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import 必要的模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据 & 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('diabetes.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Pregnancies属性的不同取值和出现的次数\n",
      "1     135\n",
      "0     111\n",
      "2     103\n",
      "3      75\n",
      "4      68\n",
      "5      57\n",
      "6      50\n",
      "7      45\n",
      "8      38\n",
      "9      28\n",
      "10     24\n",
      "11     11\n",
      "13     10\n",
      "12      9\n",
      "14      2\n",
      "15      1\n",
      "17      1\n",
      "Name: Pregnancies, dtype: int64\n",
      "\n",
      "Glucose属性的不同取值和出现的次数\n",
      "100    17\n",
      "99     17\n",
      "129    14\n",
      "125    14\n",
      "111    14\n",
      "106    14\n",
      "95     13\n",
      "108    13\n",
      "105    13\n",
      "102    13\n",
      "112    13\n",
      "122    12\n",
      "109    12\n",
      "107    11\n",
      "117    11\n",
      "90     11\n",
      "120    11\n",
      "114    11\n",
      "124    11\n",
      "128    11\n",
      "119    11\n",
      "115    10\n",
      "84     10\n",
      "91      9\n",
      "92      9\n",
      "123     9\n",
      "146     9\n",
      "126     9\n",
      "103     9\n",
      "101     9\n",
      "       ..\n",
      "75      2\n",
      "76      2\n",
      "77      2\n",
      "170     2\n",
      "195     2\n",
      "57      2\n",
      "174     2\n",
      "175     2\n",
      "188     2\n",
      "153     2\n",
      "159     2\n",
      "62      1\n",
      "72      1\n",
      "56      1\n",
      "44      1\n",
      "65      1\n",
      "61      1\n",
      "198     1\n",
      "67      1\n",
      "190     1\n",
      "149     1\n",
      "191     1\n",
      "186     1\n",
      "182     1\n",
      "178     1\n",
      "177     1\n",
      "172     1\n",
      "169     1\n",
      "160     1\n",
      "199     1\n",
      "Name: Glucose, Length: 136, dtype: int64\n",
      "\n",
      "BloodPressure属性的不同取值和出现的次数\n",
      "70     57\n",
      "74     52\n",
      "68     45\n",
      "78     45\n",
      "72     44\n",
      "64     43\n",
      "80     40\n",
      "76     39\n",
      "60     37\n",
      "0      35\n",
      "62     34\n",
      "66     30\n",
      "82     30\n",
      "88     25\n",
      "84     23\n",
      "90     22\n",
      "86     21\n",
      "58     21\n",
      "50     13\n",
      "56     12\n",
      "52     11\n",
      "54     11\n",
      "92      8\n",
      "75      8\n",
      "65      7\n",
      "94      6\n",
      "85      6\n",
      "48      5\n",
      "44      4\n",
      "96      4\n",
      "110     3\n",
      "100     3\n",
      "98      3\n",
      "106     3\n",
      "108     2\n",
      "104     2\n",
      "30      2\n",
      "55      2\n",
      "46      2\n",
      "40      1\n",
      "38      1\n",
      "24      1\n",
      "95      1\n",
      "61      1\n",
      "102     1\n",
      "114     1\n",
      "122     1\n",
      "Name: BloodPressure, dtype: int64\n",
      "\n",
      "SkinThickness属性的不同取值和出现的次数\n",
      "0     227\n",
      "32     31\n",
      "30     27\n",
      "27     23\n",
      "23     22\n",
      "33     20\n",
      "18     20\n",
      "28     20\n",
      "31     19\n",
      "39     18\n",
      "19     18\n",
      "29     17\n",
      "37     16\n",
      "26     16\n",
      "22     16\n",
      "40     16\n",
      "25     16\n",
      "35     15\n",
      "41     15\n",
      "36     14\n",
      "15     14\n",
      "17     14\n",
      "20     13\n",
      "24     12\n",
      "42     11\n",
      "13     11\n",
      "21     10\n",
      "34      8\n",
      "46      8\n",
      "38      7\n",
      "12      7\n",
      "14      6\n",
      "16      6\n",
      "11      6\n",
      "43      6\n",
      "45      6\n",
      "10      5\n",
      "44      5\n",
      "48      4\n",
      "47      4\n",
      "50      3\n",
      "49      3\n",
      "54      2\n",
      "52      2\n",
      "7       2\n",
      "8       2\n",
      "60      1\n",
      "56      1\n",
      "63      1\n",
      "51      1\n",
      "99      1\n",
      "Name: SkinThickness, dtype: int64\n",
      "\n",
      "Insulin属性的不同取值和出现的次数\n",
      "0      374\n",
      "105     11\n",
      "140      9\n",
      "130      9\n",
      "120      8\n",
      "100      7\n",
      "94       7\n",
      "180      7\n",
      "110      6\n",
      "115      6\n",
      "135      6\n",
      "66       5\n",
      "49       5\n",
      "56       5\n",
      "76       5\n",
      "210      5\n",
      "90       4\n",
      "88       4\n",
      "125      4\n",
      "71       4\n",
      "200      4\n",
      "155      4\n",
      "64       4\n",
      "160      4\n",
      "168      4\n",
      "165      4\n",
      "54       4\n",
      "190      4\n",
      "36       3\n",
      "182      3\n",
      "      ... \n",
      "191      1\n",
      "166      1\n",
      "188      1\n",
      "184      1\n",
      "171      1\n",
      "119      1\n",
      "255      1\n",
      "318      1\n",
      "91       1\n",
      "310      1\n",
      "81       1\n",
      "304      1\n",
      "300      1\n",
      "183      1\n",
      "86       1\n",
      "291      1\n",
      "89       1\n",
      "284      1\n",
      "280      1\n",
      "258      1\n",
      "278      1\n",
      "277      1\n",
      "275      1\n",
      "274      1\n",
      "272      1\n",
      "271      1\n",
      "270      1\n",
      "108      1\n",
      "112      1\n",
      "846      1\n",
      "Name: Insulin, Length: 186, dtype: int64\n",
      "\n",
      "BMI属性的不同取值和出现的次数\n",
      "32.0    13\n",
      "31.6    12\n",
      "31.2    12\n",
      "0.0     11\n",
      "33.3    10\n",
      "32.4    10\n",
      "32.8     9\n",
      "30.8     9\n",
      "32.9     9\n",
      "30.1     9\n",
      "29.7     8\n",
      "33.6     8\n",
      "34.2     8\n",
      "30.4     7\n",
      "35.5     7\n",
      "27.6     7\n",
      "33.2     7\n",
      "28.7     7\n",
      "25.9     7\n",
      "39.4     7\n",
      "30.0     7\n",
      "30.5     7\n",
      "27.8     7\n",
      "25.2     6\n",
      "36.8     6\n",
      "28.9     6\n",
      "34.9     6\n",
      "24.2     6\n",
      "34.3     6\n",
      "38.5     6\n",
      "        ..\n",
      "21.7     1\n",
      "21.2     1\n",
      "43.1     1\n",
      "45.4     1\n",
      "40.7     1\n",
      "45.2     1\n",
      "24.1     1\n",
      "44.1     1\n",
      "29.2     1\n",
      "38.6     1\n",
      "67.1     1\n",
      "41.2     1\n",
      "26.7     1\n",
      "48.8     1\n",
      "49.6     1\n",
      "46.7     1\n",
      "41.8     1\n",
      "22.7     1\n",
      "24.9     1\n",
      "40.8     1\n",
      "57.3     1\n",
      "31.1     1\n",
      "53.2     1\n",
      "46.3     1\n",
      "36.2     1\n",
      "32.1     1\n",
      "52.9     1\n",
      "31.3     1\n",
      "45.7     1\n",
      "42.8     1\n",
      "Name: BMI, Length: 248, dtype: int64\n",
      "\n",
      "DiabetesPedigreeFunction属性的不同取值和出现的次数\n",
      "0.254    6\n",
      "0.258    6\n",
      "0.259    5\n",
      "0.238    5\n",
      "0.207    5\n",
      "0.268    5\n",
      "0.261    5\n",
      "0.167    4\n",
      "0.190    4\n",
      "0.270    4\n",
      "0.687    4\n",
      "0.237    4\n",
      "0.263    4\n",
      "0.284    4\n",
      "0.245    4\n",
      "0.260    4\n",
      "0.692    4\n",
      "0.304    4\n",
      "0.299    4\n",
      "0.551    4\n",
      "0.197    4\n",
      "0.349    3\n",
      "0.205    3\n",
      "0.256    3\n",
      "0.187    3\n",
      "0.337    3\n",
      "0.142    3\n",
      "0.422    3\n",
      "0.340    3\n",
      "0.389    3\n",
      "        ..\n",
      "0.725    1\n",
      "0.325    1\n",
      "0.239    1\n",
      "1.258    1\n",
      "0.331    1\n",
      "0.096    1\n",
      "0.832    1\n",
      "0.454    1\n",
      "0.546    1\n",
      "0.704    1\n",
      "0.398    1\n",
      "0.711    1\n",
      "0.547    1\n",
      "0.426    1\n",
      "0.491    1\n",
      "0.503    1\n",
      "0.867    1\n",
      "0.944    1\n",
      "0.743    1\n",
      "0.666    1\n",
      "0.420    1\n",
      "0.588    1\n",
      "1.144    1\n",
      "0.593    1\n",
      "0.162    1\n",
      "0.886    1\n",
      "0.804    1\n",
      "1.251    1\n",
      "0.382    1\n",
      "0.375    1\n",
      "Name: DiabetesPedigreeFunction, Length: 517, dtype: int64\n",
      "\n",
      "Age属性的不同取值和出现的次数\n",
      "22    72\n",
      "21    63\n",
      "25    48\n",
      "24    46\n",
      "23    38\n",
      "28    35\n",
      "26    33\n",
      "27    32\n",
      "29    29\n",
      "31    24\n",
      "41    22\n",
      "30    21\n",
      "37    19\n",
      "42    18\n",
      "33    17\n",
      "32    16\n",
      "36    16\n",
      "38    16\n",
      "45    15\n",
      "34    14\n",
      "40    13\n",
      "43    13\n",
      "46    13\n",
      "39    12\n",
      "35    10\n",
      "50     8\n",
      "44     8\n",
      "51     8\n",
      "52     8\n",
      "58     7\n",
      "47     6\n",
      "54     6\n",
      "57     5\n",
      "60     5\n",
      "48     5\n",
      "49     5\n",
      "53     5\n",
      "55     4\n",
      "62     4\n",
      "63     4\n",
      "66     4\n",
      "56     3\n",
      "59     3\n",
      "65     3\n",
      "67     3\n",
      "61     2\n",
      "69     2\n",
      "72     1\n",
      "64     1\n",
      "68     1\n",
      "70     1\n",
      "81     1\n",
      "Name: Age, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "categorical_features = data.ix[:,:-1].columns\n",
    "for col in categorical_features:\n",
    "    print '\\n%s属性的不同取值和出现的次数'%col\n",
    "    print data[col].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 离群点检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a09d1add0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pregnancies散点图\n",
    "plt.scatter(data.Pregnancies, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"Pregnancies Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a09db1850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pregnancies直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Pregnancies.values, bins=30, kde=True)\n",
    "plt.xlabel('Pregnancies Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12d62610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Glucose散点图\n",
    "plt.scatter(data.Glucose, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"Glucose Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12f39d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Glucose直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Glucose.values, bins=30, kde=True)\n",
    "plt.xlabel('Glucose Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12e59f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BloodPressure散点图\n",
    "plt.scatter(data.BloodPressure, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"BloodPressure Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12e6ef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BloodPressure直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.BloodPressure.values, bins=30, kde=True)\n",
    "plt.xlabel('BloodPressure Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12d50850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SkinThickness散点图\n",
    "plt.scatter(data.SkinThickness, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"SkinThickness Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a130a2b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SkinThickness直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.SkinThickness.values, bins=30, kde=True)\n",
    "plt.xlabel('SkinThickness Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ETgzyWtpfsFcwFOBBwMvmmrGqtqQ5E3AOTRXLp1UVZcnOQYYrao1U80vSZp7meDo0P0bJc+muX12gMur6txBFzaZkZGRGh11Z0KSZiLJJVU17eH5fm9zcR5w3iZXJUl6QJvtbS4kSQuQoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqTOQEMhyX5Jrk6yNsmRE0z/b0muSHJZknOTPGqQ9UiSpjawUEiyBXACsD+wGjgsyepxzb4DjFTVHsAZwPsHVY8kaXqD3FPYC1hbVddU1T3A6cDBvQ2q6utVdVc7eAGwfID1SJKmMchQ2BW4sWd4rB03mcOBL080IckRSUaTjK5bt24zlihJ6jXIUMgE42rChsnLgRHg2ImmV9WJVTVSVSPLli3bjCVKknotGeCyx4AVPcPLgZvGN0ryXOCdwDOr6u4B1iNJmsYg9xQuBlYl2S3JVsChwJreBkn2BD4MHFRVtwywFklSHwYWClW1AXgTcA5wJfDpqro8yTFJDmqbHQtsC3wmyXeTrJlkcZKkOTDIw0dU1dnA2ePGvavn/XMHuX5J0sz4i2ZJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUmegoZBkvyRXJ1mb5MgJpm+d5FPt9AuTrNz8Nfha7K/ttpt429huu8Gua7LlT1ZPv3VNN/9slr0py5xrg/wMC6F/NtXAQiHJFsAJwP7AauCwJKvHNTscuK2qHgMcD/z9oOrR4rV+/czGb651zXa9mzp9NvMOoi8GZZCfYSH0z6Ya5J7CXsDaqrqmqu4BTgcOHtfmYOCU9v0ZwHOSZIA1SZKmMMhQ2BW4sWd4rB03YZuq2gDcAew4fkFJjkgymmR03bp1AypXkjTIUJjoG3/Nog1VdWJVjVTVyLJlyzZLcZKk+xtkKIwBK3qGlwM3TdYmyRLgYcDPB1iTJGkKgwyFi4FVSXZLshVwKLBmXJs1wKva94cA51XV/fYUpE2xdOnMxm+udc12vZs6fTbzDqIvBmWQn2Eh9M+mWjKoBVfVhiRvAs4BtgBOqqrLkxwDjFbVGuBjwKlJ1tLsIRy6+evY3EvUQnHnnQ/M5Q+yrkF/5rlg/wzWwEIBoKrOBs4eN+5dPe9/DbxkkDVIkvrnL5olSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ3Mtx8QJ1kHXD/L2XcCbt2M5Sw09s/U7J+p2T9TG3b/PKqqpr153LwLhU2RZLSqRoZdxwOV/TM1+2dq9s/U5kv/ePhIktQxFCRJncUWCicOu4AHOPtnavbP1Oyfqc2L/llU5xQkSVNbbHsKkqQpGAqSpM6iCYUk+yW5OsnaJEcOu55hSLIiydeTXJnk8iR/0Y7fIcn/TfLD9t+Ht+OT5INtn12W5MnD/QSDl2SLJN9JclY7vFuSC9u++VT7FEGSbN0Or22nrxxm3XMhyfZJzkhyVbsN7eO281tJ3tr+f/X9JJ9Mss183H4WRSgk2QI4AdgfWA0clmT1cKsaig3A26rq8cDewBvbfjgSOLeqVgHntsPQ9Neq9nUE8KG5L3nO/QVwZc/w3wPHt31zG3B4O/5w4LaqegxwfNtuofsA8JWqehzwhzT95LYDJNkVeAswUlVPpHna5KHMx+2nqhb8C9gHOKdn+CjgqGHXNewX8AXgecDVwM7tuJ2Bq9v3HwYO62nftVuIL2A5zR+2ZwNnAaH5BeqS8dsRzWNm92nfL2nbZdifYYB9sx1w7fjP6LbTfb5dgRuBHdrt4Szgj+fj9rMo9hT47X+wjcbacYtWu7u6J3Ah8Miq+glA++8j2maLrd/+EXgH8Jt2eEfg9qra0A73fv6ub9rpd7TtF6pHA+uAj7eH1z6a5KG47QBQVT8GjgNuAH5Csz1cwjzcfhZLKGSCcYv2Wtwk2wKfBf6yqqZ6VPmi6bckBwK3VNUlvaMnaFp9TFuIlgBPBj5UVXsCv+S3h4omsqj6pz2XcjCwG7AL8FCaQ2jjPeC3n8USCmPAip7h5cBNQ6plqJJsSRMIp1XVme3om5Ps3E7fGbilHb+Y+u3pwEFJrgNOpzmE9I/A9kmWtG16P3/XN+30hwE/n8uC59gYMFZVF7bDZ9CEhNtO47nAtVW1rqruBc4EnsY83H4WSyhcDKxqrwTYiuYE0Joh1zTnkgT4GHBlVf1Dz6Q1wKva96+iOdewcfwr2ytJ9gbu2HioYKGpqqOqanlVraTZPs6rqpcBXwcOaZuN75uNfXZI2/4B8U1vEKrqp8CNSf6gHfUc4Arcdja6Adg7yUPa/8829s/8236GfVJjDk8EHQD8APgR8M5h1zOkPvgjml3Uy4Dvtq8DaI5lngv8sP13h7Z9aK7a+hHwPZorK4b+Oeagn/YFzmrfPxq4CFgLfAbYuh2/TTu8tp3+6GHXPQf98iRgtN1+Pg883G3nd/rnb4CrgO8DpwJbz8ftx9tcSJI6i+XwkSSpD4aCJKljKEiSOoaCJKljKEiSOoaC5rUkv9jMy1uZ5Pvt+5EkH5zh/K9N8r32zqDfT3LwLGrYPsmfj6vpT2a6HGk2DAVpElU1WlVv6bd9kuXAO4E/qqo9aO5Ee9ksVr098Oc9wyuBGYVCe2dgacYMBS0ISfZN8o2e+/2f1v6ylCTvS3JF++39uHbcyUkO6Zn/fnsc7TI3Plfh6CQnteu4JslEYfEIYD3wC4Cq+kVVXdvO/5gkX0tyaZJvJ9k9ybZJzm2Hv9ezV/E+YPck301ybDv8n9rht6Z55sOxSS5uP9Of9tT79SSfoPnBmDRjS6ZvIs0bewJPoLm/zPnA05NcAbwIeFxVVZLtN2H5jwOeBSwFrk7yoWruc7PRpcDNwLVJzgXOrKovttNOA95XVZ9Lsg3NF7J7gBdV1Z1JdgIuSLKG5kZzT6yqJ0Hzxx54e1Ud2A4fQXPbiKcm2Ro4P8lX2/Xs1c577SZ8Ti1i7iloIbmoqsaq6jc0t/BYCdwJ/Br4aJIXA3dtwvK/VFV3V9WtNDd+e2TvxKq6D9iP5l42PwCOb/cwlgK7VtXn2na/rqq7aG4F8Z4klwFfo7md8u8scxLPp7mv0Hdpbn2+I83DbKDpAwNBs2YoaCG5u+f9fTQPN9lA8+35s8ALga+00zfQbv/tYaatZrP88Q2qcVFVvZfmxnr/hYlvkwzwMmAZ8JR2r+BmmnviTCfAm6vqSe1rt6rauKfwyz7mlyZlKGhBa58d8bCqOhv4S5qbugFcBzylfX8wsOVmWNcu+d1nET8JuL6aZ1aMJXlh227rJA+huV3yLVV1b5JnAY9q51tPc4iKSYbPAd7Q3gadJI9tH3gjbTLPKWihWwp8oT2OH+Ct7fiPtOMvorm75+b4hr0lcFySXWgOWa0D/qyd9grgw0mOAe4FXkJznuGLSUZpDnddBVBVP0tyfntp7JeB/w5sSHIpcDLNs5JXAt9u93LW0ewFSZvMu6RKkjoePpIkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdf4/pOpBy11bB1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12feb650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Insulin散点图\n",
    "plt.scatter(data.Insulin, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"Insulin Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a131fc6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Insulin直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Insulin.values, bins=30, kde=True)\n",
    "plt.xlabel('Insulin Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a133e6b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI散点图\n",
    "plt.scatter(data.BMI, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"BMI Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13410ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.BMI.values, bins=30, kde=True)\n",
    "plt.xlabel('BMI Distplot', fontsize=12)\n",
    "plt.show()\n",
    "#剔除离群点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a134f3690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DiabetesPedigreeFunction散点图\n",
    "plt.scatter(data.DiabetesPedigreeFunction, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"DiabetesPedigreeFunction Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a134d4250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DiabetesPedigreeFunction直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.DiabetesPedigreeFunction.values, bins=30, kde=True)\n",
    "plt.xlabel('DiabetesPedigreeFunction Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13618b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age散点图\n",
    "plt.scatter(data.Age, data.Outcome, c = \"blue\", marker = \"s\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"Age Scatter\")\n",
    "plt.ylabel(\"Outcome\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a135fae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Age.values, bins=30, kde=True)\n",
    "plt.xlabel('Age Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13618610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Outcome散点图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Outcome.values, bins=30, kde=True)\n",
    "plt.xlabel('Outcome Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据初步分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0      148             72             35        0  33.6   \n",
       "1       85             66             29        0  26.6   \n",
       "2      183             64              0        0  23.3   \n",
       "3       89             66             23       94  28.1   \n",
       "4      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过对各列特征数据的分析，缺失值对数据准确性的干扰很大，Pregnancies这个特征中的0是合理值，\n",
    "# Outcome是类别标签，其他特征都应该对缺失值进行处理，这里先对各特征做一下分析\n",
    "# 先统计一下所有特征中带有缺失值的样本数量\n",
    "# data1是剔除掉Pregnancies特征和Outcome的DF\n",
    "data1 = data.iloc[:,1:-1]\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Glucose特征中含有5个0，占总样本数比例为0.000000\n",
      "\n",
      "BloodPressure特征中含有35个0，占总样本数比例为5.000000\n",
      "\n",
      "SkinThickness特征中含有227个0，占总样本数比例为32.000000\n",
      "\n",
      "Insulin特征中含有374个0，占总样本数比例为53.000000\n",
      "\n",
      "BMI特征中含有11个0，占总样本数比例为1.000000\n",
      "\n",
      "DiabetesPedigreeFunction特征中含有0个0，占总样本数比例为0.000000\n",
      "\n",
      "Age特征中含有0个0，占总样本数比例为0.000000\n"
     ]
    }
   ],
   "source": [
    "# 统计所有特征中缺失值的比例\n",
    "Col = data1.columns\n",
    "for co in Col:\n",
    "    Nu=data1[data1[co].isin([0])].shape[0]\n",
    "    print '\\n%s特征中含有%d个0，占总样本数比例为%f'% (co, Nu, Nu/data1.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 724 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 724 non-null int64\n",
      "Glucose                     724 non-null int64\n",
      "BloodPressure               724 non-null int64\n",
      "SkinThickness               724 non-null int64\n",
      "Insulin                     724 non-null int64\n",
      "BMI                         724 non-null float64\n",
      "DiabetesPedigreeFunction    724 non-null float64\n",
      "Age                         724 non-null int64\n",
      "Outcome                     724 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 56.6 KB\n"
     ]
    }
   ],
   "source": [
    "# 通过分析发现SkinThickness和Insulin特征的缺失值比例相当高，Glucose、BloodPressure、BMI少量样本，\n",
    "# 现在对特征值进行一下处理，Glucose、BloodPressure、BMI的缺失值样本直接删掉，SkinThickness和Insulin用均值替换\n",
    "dr_col = ['Glucose','BMI','BloodPressure']\n",
    "for co in dr_col:\n",
    "    data = data[data[co] != 0]\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.191</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>168</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.537</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>139</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1.441</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>846</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.398</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>19</td>\n",
       "      <td>175</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.587</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>84</td>\n",
       "      <td>47</td>\n",
       "      <td>230</td>\n",
       "      <td>45.8</td>\n",
       "      <td>0.551</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>107</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.183</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "      <td>96</td>\n",
       "      <td>34.6</td>\n",
       "      <td>0.529</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>88</td>\n",
       "      <td>41</td>\n",
       "      <td>235</td>\n",
       "      <td>39.3</td>\n",
       "      <td>0.704</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8</td>\n",
       "      <td>99</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.388</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7</td>\n",
       "      <td>196</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>0.451</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>80</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.263</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>33</td>\n",
       "      <td>146</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10</td>\n",
       "      <td>125</td>\n",
       "      <td>70</td>\n",
       "      <td>26</td>\n",
       "      <td>115</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.205</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7</td>\n",
       "      <td>147</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.257</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>140</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.487</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13</td>\n",
       "      <td>145</td>\n",
       "      <td>82</td>\n",
       "      <td>19</td>\n",
       "      <td>110</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>117</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.337</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5</td>\n",
       "      <td>109</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.546</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>158</td>\n",
       "      <td>76</td>\n",
       "      <td>36</td>\n",
       "      <td>245</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.851</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>88</td>\n",
       "      <td>58</td>\n",
       "      <td>11</td>\n",
       "      <td>54</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0.267</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>60</td>\n",
       "      <td>17</td>\n",
       "      <td>160</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.453</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.293</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>11</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>37</td>\n",
       "      <td>150</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.785</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>94</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>58</td>\n",
       "      <td>18</td>\n",
       "      <td>116</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.219</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>9</td>\n",
       "      <td>140</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.734</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>13</td>\n",
       "      <td>153</td>\n",
       "      <td>88</td>\n",
       "      <td>37</td>\n",
       "      <td>140</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1.174</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>84</td>\n",
       "      <td>33</td>\n",
       "      <td>105</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>94</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>49.3</td>\n",
       "      <td>0.358</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>74</td>\n",
       "      <td>41</td>\n",
       "      <td>57</td>\n",
       "      <td>46.3</td>\n",
       "      <td>1.096</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>3</td>\n",
       "      <td>187</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>200</td>\n",
       "      <td>36.4</td>\n",
       "      <td>0.408</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>6</td>\n",
       "      <td>162</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.178</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>4</td>\n",
       "      <td>136</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>1.182</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>78</td>\n",
       "      <td>39</td>\n",
       "      <td>74</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>3</td>\n",
       "      <td>108</td>\n",
       "      <td>62</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.223</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>510</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.222</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>8</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.443</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>88</td>\n",
       "      <td>39</td>\n",
       "      <td>110</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1.057</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>7</td>\n",
       "      <td>137</td>\n",
       "      <td>90</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.391</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.258</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.197</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.278</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>58</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.766</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.142</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>724 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "5              5      116             74              0        0  25.6   \n",
       "6              3       78             50             32       88  31.0   \n",
       "7              2      197             70             45      543  30.5   \n",
       "8              4      110             92              0        0  37.6   \n",
       "9             10      168             74              0        0  38.0   \n",
       "10            10      139             80              0        0  27.1   \n",
       "11             1      189             60             23      846  30.1   \n",
       "12             5      166             72             19      175  25.8   \n",
       "13             0      118             84             47      230  45.8   \n",
       "14             7      107             74              0        0  29.6   \n",
       "15             1      103             30             38       83  43.3   \n",
       "16             1      115             70             30       96  34.6   \n",
       "17             3      126             88             41      235  39.3   \n",
       "18             8       99             84              0        0  35.4   \n",
       "19             7      196             90              0        0  39.8   \n",
       "20             9      119             80             35        0  29.0   \n",
       "21            11      143             94             33      146  36.6   \n",
       "22            10      125             70             26      115  31.1   \n",
       "23             7      147             76              0        0  39.4   \n",
       "24             1       97             66             15      140  23.2   \n",
       "25            13      145             82             19      110  22.2   \n",
       "26             5      117             92              0        0  34.1   \n",
       "27             5      109             75             26        0  36.0   \n",
       "28             3      158             76             36      245  31.6   \n",
       "29             3       88             58             11       54  24.8   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "694            2       99             60             17      160  36.6   \n",
       "695            1      102             74              0        0  39.5   \n",
       "696           11      120             80             37      150  42.3   \n",
       "697            3      102             44             20       94  30.8   \n",
       "698            1      109             58             18      116  28.5   \n",
       "699            9      140             94              0        0  32.7   \n",
       "700           13      153             88             37      140  40.6   \n",
       "701           12      100             84             33      105  30.0   \n",
       "702            1      147             94             41        0  49.3   \n",
       "703            1       81             74             41       57  46.3   \n",
       "704            3      187             70             22      200  36.4   \n",
       "705            6      162             62              0        0  24.3   \n",
       "706            4      136             70              0        0  31.2   \n",
       "707            1      121             78             39       74  39.0   \n",
       "708            3      108             62             24        0  26.0   \n",
       "709            0      181             88             44      510  43.3   \n",
       "710            8      154             78             32        0  32.4   \n",
       "711            1      128             88             39      110  36.5   \n",
       "712            7      137             90             41        0  32.0   \n",
       "713            0      123             72              0        0  36.3   \n",
       "714            1      106             76              0        0  37.5   \n",
       "715            6      190             92              0        0  35.5   \n",
       "716            2       88             58             26       16  28.4   \n",
       "717            9      170             74             31        0  44.0   \n",
       "718            9       89             62              0        0  22.5   \n",
       "719           10      101             76             48      180  32.9   \n",
       "720            2      122             70             27        0  36.8   \n",
       "721            5      121             72             23      112  26.2   \n",
       "722            1      126             60              0        0  30.1   \n",
       "723            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "5                       0.201   30        0  \n",
       "6                       0.248   26        1  \n",
       "7                       0.158   53        1  \n",
       "8                       0.191   30        0  \n",
       "9                       0.537   34        1  \n",
       "10                      1.441   57        0  \n",
       "11                      0.398   59        1  \n",
       "12                      0.587   51        1  \n",
       "13                      0.551   31        1  \n",
       "14                      0.254   31        1  \n",
       "15                      0.183   33        0  \n",
       "16                      0.529   32        1  \n",
       "17                      0.704   27        0  \n",
       "18                      0.388   50        0  \n",
       "19                      0.451   41        1  \n",
       "20                      0.263   29        1  \n",
       "21                      0.254   51        1  \n",
       "22                      0.205   41        1  \n",
       "23                      0.257   43        1  \n",
       "24                      0.487   22        0  \n",
       "25                      0.245   57        0  \n",
       "26                      0.337   38        0  \n",
       "27                      0.546   60        0  \n",
       "28                      0.851   28        1  \n",
       "29                      0.267   22        0  \n",
       "..                        ...  ...      ...  \n",
       "694                     0.453   21        0  \n",
       "695                     0.293   42        1  \n",
       "696                     0.785   48        1  \n",
       "697                     0.400   26        0  \n",
       "698                     0.219   22        0  \n",
       "699                     0.734   45        1  \n",
       "700                     1.174   39        0  \n",
       "701                     0.488   46        0  \n",
       "702                     0.358   27        1  \n",
       "703                     1.096   32        0  \n",
       "704                     0.408   36        1  \n",
       "705                     0.178   50        1  \n",
       "706                     1.182   22        1  \n",
       "707                     0.261   28        0  \n",
       "708                     0.223   25        0  \n",
       "709                     0.222   26        1  \n",
       "710                     0.443   45        1  \n",
       "711                     1.057   37        1  \n",
       "712                     0.391   39        0  \n",
       "713                     0.258   52        1  \n",
       "714                     0.197   26        0  \n",
       "715                     0.278   66        1  \n",
       "716                     0.766   22        0  \n",
       "717                     0.403   43        1  \n",
       "718                     0.142   33        0  \n",
       "719                     0.171   63        0  \n",
       "720                     0.340   27        0  \n",
       "721                     0.245   30        0  \n",
       "722                     0.349   47        1  \n",
       "723                     0.315   23        0  \n",
       "\n",
       "[724 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对SkinThickness和Insulin的特征样本均值进行替换\n",
    "ST_mean = np.mean(data['SkinThickness'])\n",
    "Is_mean = np.mean(data['Insulin'])\n",
    "\n",
    "data['SkinThickness'].replace(0,ST_mean,inplace = True)\n",
    "data['Insulin'].replace(0,Is_mean,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a134313d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看做均值替换后的SkinThickness直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.SkinThickness.values, bins=30, kde=True)\n",
    "plt.xlabel('SkinThickness Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13410d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看做均值替换后的Insulin直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Insulin.values, bins=30, kde=True)\n",
    "plt.xlabel('Insulin Distplot', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#随机选择 20%的数据作为测试集\n",
    "X,y = data.iloc[:,0:8],data[['Outcome']]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(train_test_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145, 145, 579, 579)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据集进行标准化处理\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "y_test.shape[0],X_test.shape[0],X_train.shape[0],y_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Pregnancies', u'Glucose', u'BloodPressure', u'SkinThickness',\n",
       "       u'Insulin', u'BMI', u'DiabetesPedigreeFunction', u'Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns[0:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转化格式\n",
    "X_train = pd.DataFrame(data = X_train,columns= data.columns[0:8])\n",
    "y_train = pd.DataFrame(data = y_train,columns= ['Outcome'])\n",
    "X_test = pd.DataFrame(data = X_test,columns= data.columns[0:8])\n",
    "y_train = pd.DataFrame(data = y_train,columns= ['Outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把做好数据集存盘\n",
    "X_train = X_train.to_csv('X_train.csv', index= False)\n",
    "X_test = X_test.to_csv('X_test.csv', index= False)\n",
    "y_train = y_train.to_csv('y_train.csv', index= False)\n",
    "y_test = y_test.to_csv('y_test.csv', index= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查询数据维度\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a135fa390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 输出热图\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('Diabetes.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies and Age = 0.56\n",
      "SkinThickness and BMI = 0.55\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a09082290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a131de650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of only the highly correlated pairs\n",
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(data, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "X_train = pd.read_csv('X_train.csv')\n",
    "y_train = pd.read_csv('y_train.csv')\n",
    "#X_train.info()\n",
    "#y_train.info()\n",
    "y_train = y_train.values\n",
    "c, r = y_train.shape\n",
    "y_train = y_train.reshape(c,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### default Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.37881712 0.54028223 0.56953556 0.42274254 0.46885164]\n",
      "cv logloss is: 0.4760458184015011\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print 'logloss of each fold is: ',-loss\n",
    "print 'cv logloss is:', -loss.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则化的 Logistic Regression及参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#logistic回归的需要调整超参数有：C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） 目标函数为：J = sum(logloss(f(xi), yi)) + C* penalty在sklearn框架下，不同学习器的参数调整步骤相同： 设置候选参数集合 调用GridSearchCV 调用fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/cuiyue/anaconda2/lib/python2.7/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00278773, 0.00315223, 0.00220141, 0.00196395, 0.00156913,\n",
       "        0.0017499 , 0.00300822, 0.00329947, 0.00271235, 0.00304742,\n",
       "        0.00306425, 0.00189157, 0.00276012, 0.00244045]),\n",
       " 'mean_score_time': array([0.0013196 , 0.00162382, 0.0015502 , 0.00118861, 0.00087218,\n",
       "        0.00144339, 0.00143952, 0.00135717, 0.00181022, 0.0019206 ,\n",
       "        0.00108089, 0.00110903, 0.00171986, 0.0014502 ]),\n",
       " 'mean_test_score': array([-0.69314718, -0.64141913, -0.673573  , -0.52663202, -0.4805596 ,\n",
       "        -0.47565323, -0.4755837 , -0.47593186, -0.47678869, -0.47683776,\n",
       "        -0.47694033, -0.47694627, -0.47695948, -0.47695732]),\n",
       " 'mean_train_score': array([-0.69314718, -0.64004793, -0.67021961, -0.51987828, -0.46649196,\n",
       "        -0.45943085, -0.45417072, -0.45401927, -0.45391732, -0.45391571,\n",
       "        -0.45391458, -0.45391456, -0.45391455, -0.45391455]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  2,  1,  3,  4,  5,  6,  7,  9,  8],\n",
       "       dtype=int32),\n",
       " 'split0_test_score': array([-0.69314718, -0.63372459, -0.69314718, -0.49002025, -0.40607632,\n",
       "        -0.39723169, -0.3793467 , -0.37881712, -0.37683695, -0.37679966,\n",
       "        -0.37660339, -0.37659669, -0.37657791, -0.37657637]),\n",
       " 'split0_train_score': array([-0.69314718, -0.64491236, -0.69314718, -0.53519271, -0.48887144,\n",
       "        -0.48248252, -0.4783285 , -0.47818479, -0.47810934, -0.47810785,\n",
       "        -0.47810704, -0.47810702, -0.47810701, -0.47810701]),\n",
       " 'split1_test_score': array([-0.69314718, -0.64678168, -0.6683337 , -0.54914047, -0.52291581,\n",
       "        -0.52571041, -0.53812881, -0.54028223, -0.54331324, -0.54352898,\n",
       "        -0.54386332, -0.54388906, -0.54392195, -0.54392545]),\n",
       " 'split1_train_score': array([-0.69314718, -0.63714059, -0.66248386, -0.51071934, -0.45193853,\n",
       "        -0.4437009 , -0.43731316, -0.4371378 , -0.43700669, -0.43700488,\n",
       "        -0.43700341, -0.43700338, -0.43700337, -0.43700337]),\n",
       " 'split2_test_score': array([-0.69314718, -0.64682928, -0.66861669, -0.5561162 , -0.52982766,\n",
       "        -0.54646949, -0.56624948, -0.56953556, -0.57397645, -0.57431767,\n",
       "        -0.574805  , -0.57484406, -0.57489931, -0.57489723]),\n",
       " 'split2_train_score': array([-0.69314718, -0.63676234, -0.66562974, -0.5079064 , -0.44830934,\n",
       "        -0.43952728, -0.43280371, -0.43265773, -0.43251818, -0.4325167 ,\n",
       "        -0.43251514, -0.43251512, -0.4325151 , -0.4325151 ]),\n",
       " 'split3_test_score': array([-0.69314718, -0.6390642 , -0.67011966, -0.51085659, -0.45063958,\n",
       "        -0.43555958, -0.42353954, -0.42274254, -0.42148875, -0.42143651,\n",
       "        -0.42130868, -0.42130662, -0.42129486, -0.42129364]),\n",
       " 'split3_train_score': array([-0.69314718, -0.64124373, -0.66978065, -0.52613222, -0.47691163,\n",
       "        -0.47059049, -0.46625524, -0.4660953 , -0.46601751, -0.46601554,\n",
       "        -0.46601469, -0.46601468, -0.46601467, -0.46601467]),\n",
       " 'split4_test_score': array([-0.69314718, -0.64073647, -0.66739846, -0.52727183, -0.49389736,\n",
       "        -0.47378829, -0.47123989, -0.46885164, -0.46890938, -0.46868591,\n",
       "        -0.46870236, -0.46867592, -0.46868456, -0.468675  ]),\n",
       " 'split4_train_score': array([-0.69314718, -0.64018062, -0.66005662, -0.51944074, -0.46642888,\n",
       "        -0.46085305, -0.45615301, -0.45602074, -0.45593489, -0.45593357,\n",
       "        -0.45593264, -0.45593262, -0.45593261, -0.45593261]),\n",
       " 'std_fit_time': array([0.00122838, 0.00081264, 0.00097144, 0.0004398 , 0.00033196,\n",
       "        0.0001892 , 0.00103108, 0.00098679, 0.00092119, 0.00200692,\n",
       "        0.00126773, 0.00044971, 0.00091815, 0.00063727]),\n",
       " 'std_score_time': array([0.00035322, 0.00065718, 0.00087899, 0.00034959, 0.00011858,\n",
       "        0.00104287, 0.00066837, 0.000484  , 0.00057962, 0.00078848,\n",
       "        0.00021426, 0.00030365, 0.00063938, 0.0004987 ]),\n",
       " 'std_test_score': array([7.05795486e-17, 4.97819303e-03, 9.88891953e-03, 2.44010469e-02,\n",
       "        4.66779406e-02, 5.53633479e-02, 6.95443436e-02, 7.10962072e-02,\n",
       "        7.35446413e-02, 7.36965540e-02, 7.39582960e-02, 7.39760014e-02,\n",
       "        7.40032491e-02, 7.40041594e-02]),\n",
       " 'std_train_score': array([0.        , 0.00297852, 0.01191689, 0.01001047, 0.01517778,\n",
       "        0.01613396, 0.01717143, 0.01717619, 0.01720251, 0.01720251,\n",
       "        0.01720281, 0.01720282, 0.01720282, 0.01720282])}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47558369531312744\n",
      "{'penalty': 'l1', 'C': 1}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a135b7d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame(grid.cv_results_).to_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#cvresult = pd.DataFrame.from_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#test_means = cv_results['mean_test_score']\n",
    "#test_stds = cv_results['std_test_score'] \n",
    "#train_means = cvresult['mean_train_score']\n",
    "#train_stds = cvresult['std_train_score'] \n",
    "\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #plt.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'neg-logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用LogisticRegressionCV实现正则化的 Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L1正则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[1, 10, 100, 1000], class_weight=None, cv=5,\n",
       "           dual=False, fit_intercept=True, intercept_scaling=1.0,\n",
       "           max_iter=100, multi_class='ovr', n_jobs=1, penalty='l1',\n",
       "           random_state=None, refit=True, scoring='neg_log_loss',\n",
       "           solver='liblinear', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [1, 10,100,1000]\n",
    "\n",
    "# LogisticRegressionCV比GridSearchCV快\n",
    "lrcv_L1 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l1', solver='liblinear', multi_class='ovr')\n",
    "lrcv_L1.fit(X_train, y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.37935498, -0.37683951, -0.37660355, -0.3765806 ],\n",
       "        [-0.53812224, -0.54331025, -0.54385502, -0.54391902],\n",
       "        [-0.56624932, -0.5739805 , -0.57481406, -0.5748924 ],\n",
       "        [-0.42353961, -0.42148435, -0.42131171, -0.42129662],\n",
       "        [-0.47123576, -0.46891081, -0.4686921 , -0.46867662]])}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L1.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.45514359,  1.24853601, -0.0900236 ,  0.01031093, -0.17526035,\n",
       "         0.59040701,  0.22977609,  0.03215685]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L1.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L2正则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[1, 10, 100, 1000], class_weight=None, cv=5,\n",
       "           dual=False, fit_intercept=True, intercept_scaling=1.0,\n",
       "           max_iter=100, multi_class='ovr', n_jobs=1, penalty='l2',\n",
       "           random_state=None, refit=True, scoring='neg_log_loss',\n",
       "           solver='liblinear', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [1, 10,100,1000]\n",
    "\n",
    "# 为了和GridSeachCV比较，也用liblinear\n",
    "\n",
    "lr_cv_L2 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l2', solver='liblinear', multi_class='ovr')\n",
    "lr_cv_L2.fit(X_train, y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.37881712, -0.37679966, -0.37659669, -0.37657637],\n",
       "        [-0.54028223, -0.54352898, -0.54388906, -0.54392545],\n",
       "        [-0.56953556, -0.57431767, -0.57484406, -0.57489723],\n",
       "        [-0.42274254, -0.42143651, -0.42130662, -0.42129364],\n",
       "        [-0.46885164, -0.46868591, -0.46867592, -0.468675  ]])}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_cv_L2.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[1, 10, 100, 1000], class_weight=None, cv=5,\n",
       "           dual=False, fit_intercept=True, intercept_scaling=1.0,\n",
       "           max_iter=100, multi_class='ovr', n_jobs=1, penalty='l2',\n",
       "           random_state=None, refit=True, scoring='neg_log_loss',\n",
       "           solver='lbfgs', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [1, 10,100,1000]\n",
    "\n",
    "# LogisticRegressionCV比GridSearchCV快\n",
    "lrcv_L2 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l2', multi_class='ovr')\n",
    "lrcv_L2.fit(X_train, y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.37833443, -0.37675093, -0.37658984, -0.37657594],\n",
       "        [-0.54059701, -0.54357142, -0.54390088, -0.54393064],\n",
       "        [-0.57046397, -0.57442986, -0.5748614 , -0.57490659],\n",
       "        [-0.42250879, -0.42141273, -0.42130289, -0.42129186],\n",
       "        [-0.46860454, -0.46866232, -0.46867539, -0.4686748 ]])}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L2.scores_"
   ]
  }
 ],
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